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ISSN 1386-6338 (P)
ISSN 1434-3207 (E)
In Silico Biology is a scientific research journal for the advancement of computational models and simulations applied to complex biological phenomena. We publish peer-reviewed leading-edge biological, biomedical and biotechnological research in which computer-based (i.e.,
) modeling and analysis tools are developed and utilized to predict and elucidate dynamics of biological systems, their design and control, and their evolution. Experimental support may also be provided to support the computational analyses.
In Silico Biology aims to advance the knowledge of the principles of organization of living systems. We strive to provide computational frameworks for understanding how observable biological properties arise from complex systems. In particular, we seek for integrative formalisms to decipher cross-talks underlying systems level properties, ultimate aim of multi-scale models.
Studies published in
In Silico Biology generally use theoretical models and computational analysis to gain quantitative insights into regulatory processes and networks, cell physiology and morphology, tissue dynamics and organ systems. Special areas of interest include signal transduction and information processing, gene expression and gene regulatory networks, metabolism, proliferation, differentiation and morphogenesis, among others, and the use of multi-scale modeling to connect molecular and cellular systems to the level of organisms and populations.
In Silico Biology also publishes foundational research in which novel algorithms are developed to facilitate modeling and simulations. Such research must demonstrate application to a concrete biological problem.
In Silico Biology frequently publishes special issues on seminal topics and trends. Special issues are handled by Special Issue Editors appointed by the Editor-in-Chief. Proposals for special issues should be sent to the Editor-in-Chief.
About In Silico Biology
is a pendant to
(in the living system) and
(in the test tube) biological experiments, and implies the gain of insights by computer-based simulations and model analyses.
In Silico Biology (ISB) was founded in 1998 as a purely online journal. IOS Press became the publisher of the printed journal shortly after. Today, ISB is dedicated exclusively to biological systems modeling and multi-scale simulations and is published solely by IOS Press. The previous online publisher, Bioinformation Systems, maintains a website containing studies published between 1998 and 2010 for archival purposes.
We strongly support open communications and encourage researchers to share results and preliminary data with the community. Therefore, results and preliminary data made public through conference presentations, conference proceeding or posting of unrefereed manuscripts on preprint servers will not prohibit publication in ISB. However, authors are required to modify a preprint to include the journal reference (including DOI), and a link to the published article on the ISB website upon publication.
Abstract: A thorough understanding of the in vivo kinetics of microorganisms requires the analysis of different data sets and therefore needs support from different sources of genome, transcriptome, proteome and metabolome data, as well as to generate new data in the laboratory to depict cell phenotypes in different scenarios. The value of dynamic metabolic data depends on the adequate design of wet experiments. In this paper a schematic representation of wet dynamic experiments to generate data is…discussed. As a case study, the linking of the central metabolism with the carnitine secondary metabolism in E. coli is presented. The feed-back between the data generated and in silico modeling helps our understanding of the Escherichia coli expressed phenotype and permits new wet experiments to be designed to generate data concerning metabolic optimization.
Keywords: Data generation, cell metabolism, reactor pulsing, environmental and genetic perturbations, transcriptome, metabolic model
Abstract: The HumanPSD database on the complete proteomes of human, mouse and rat has been integrated with the databases TRANSFAC on gene regulation and TRANSPATH on signal transduction to provide a comprehensive systems biological platform for these organisms. As a next step, integration with PathoDB and PathoSign on pathologically relevant mutations is planned together with an extension beyond the limits of the individual cell, towards intercellular networks, by integrating the database EndoNet on hormonal networks as well.…The overall aim is to come up with a platform that is suitable to provide knowledge for systems pathology, i.e. a system-wide modeling of pathological states and their development.
Abstract: Systems Biology requires a tight integration of experimental data and detailed computer models to obtain a quantitative understanding of the system under study. To facilitate this integration a standardization of data and model representation and storage is important. We illustrate here such an integration using JWS Online, the modeling tool developed in our group. We follow the approach of the Silicon Cell project for the construction and validation of kinetic models and discuss some issues with…respect to storage of experimental data and models. The majority of the published kinetic models for biological systems have been developed for metabolic networks and this will be our focus in this manuscript. It is not our aim to present here an all encompassing method for data and model integration, but rather to present our work on this topic to start a discussion in which the different initiatives, methods and tools can be compared.
Abstract: This paper briefly describes the SABIO-RK database model for the storage of reaction kinetics information and the guidelines followed within the SABIO-RK project to annotate the kinetic data. Such annotations support the definition of cross links to other related databases and augment the semantics of the data stored in the database.
Keywords: Reaction kinetics, database, systems biology, biochemical networks, kinetic law
Abstract: A structural diagram, in the form of a two-dimensional (2-D) sketch, remains the most effective portrait of a "small molecule" or chemical reaction. However, such structural diagrams, as for any other core data, cannot be used in speech (and should not be used in free text). "Good annotation practice" for biological databases is to use either consistent and widely recognised terminology or unique identifiers from a dedicated database to refer to the molecule of interest. Ideally,…scientists should use terminology that is both pronounceable and meaningful. Thus, a viable solution for a bioinformatician is to use a definitive controlled vocabulary of biochemical compounds and reactions, which contains both systematic and common names. In addition, chemical ontologies provide a means for placing entities of interest into wider chemical, biological or medical contexts. We present some challenges and achievements in the standardisation of chemical language in biological databases, with emphasis on three aspects of annotation: 1. good drawing practice: how to draw unambiguous 2-D diagrams; 2. good naming practice: how to give most appropriate names; and 3. good ontology practice: how to link the entity of interest by defined logical relationships to other entities.
Abstract: Systems level investigation of genomic and proteomic scale information requires incomparably higher demands for data quality than in previous decades. Truly integrated databases that deal with heterogeneous data need to be developed to be able to retrieve properties of genes, for kinetics of enzymes, for behaviour of complex networks and for the analysis and modelling of complex biological processes. Despite the fast paced global efforts in biological systems research, the current analyses are limited by the…lack of available systematic collections of comparable functional enzyme data. Besides its reliability, these data have to provide defined minimum experimental information, they must be available from the literature along with their accepted enzyme names, and must be as comprehensive as possible. However, the reality reveals a different picture: the quality of experimental data of enzymes is insufficient for the needs of systems level investigations. A 2003 founded working group, called STRENDA, recently published suggestions which intend both to improve the quality of reporting functional enzyme data and to support the comparability of inter alia enzyme kinetics for their application in the in silico investigation of biological systems.
Keywords: Enzymology, systems biology, functional enzyme data, modelling, simulation, STRENDA, comparability of data, minimum experimental information
Abstract: Computational approaches to biochemistry like modeling and simulation are dependent on the availability of kinetic information. This information can either be directly derived from experimental data generated by collaborators or has to be digged up from literature, often both. More recently, data stored in databases has started to be a valuable addition as a source of enzyme kinetic data. In order to faciliate modeling and simulation, various tools have been developed in recent years. However, automatizing…steps in setting up, analyzing or simulating models requires the data to be in defined formats. Crucial points are addressed below.
Keywords: Modeling, simulation, enzyme kinetics, database use
Abstract: We describe a workflow to translate a given metabolic network into a kinetic model; the model summarises kinetic information collected from different data sources. All reactions are modelled by convenience kinetics; where detailed kinetic laws are known, they can also be incorporated. Confidence intervals and correlations of the resulting model parameters are obtained from Bayesian parameter estimation; they can be used to sample parameter sets for Monte-Carlo simulations. The integration method ensures that…the resulting parameter distributions are thermodynamically feasible. Here we summarise different previous works on this topic: we give an overview over the convenience kinetics, thermodynamic criteria for parameter sets, Bayesian parameter estimation, the collection of kinetic data, and different machine learning techniques that can be used to obtain prior distributions for kinetic parameters. All methods have been assembled into a workflow that facilitates the integration of biochemical data and the modelling of metabolic networks from scratch.
Abstract: Understanding the logic and dynamics of gene-regulatory and biochemical networks is a major challenge for systems biology. To facilitate this research topic, we have developed CellDesigner to visualize, model and simulate biochemical networks. CellDesigner allows the users to easily create networks using solidly defined and comprehensive graphical notation. CellDesigner utilizes SBML to described models and can simulate models using an integrated SBML ODE Solver or third party simulation engine; thus enabling users…to simulate through a sophisticated graphical user interface. Although CellDesigner can integrate with existing databases (KEGG, PubMed, BioModels, etc.), by calling a web browser, or connecting to its web page through HTTP, integration with SABIO-RK has the potential to expand connectivity and semi-automate visualization and model building. SABIO-RK contains information about biochemical reactions, related kinetic equations and parameters. Also information about the experimental conditions under which these parameters were measured is stored. By using the Web service API provided by the SABIO-RK team, we have succeeded to directly connect to the database, send search queries by ID or name of its component, and then import the query results into CellDesigner.
Keywords: Systems biology, SBML, SBGN, database integration, Web services, kinetic modeling, biochemical simulation